Input
- InputTable: svm_iris_test (see SVMDense Examples Input)
- Model: densesvm_iris_linear_model (see SVMDense Example: Linear Model)
SQL Call
SELECT * FROM SVMDensePredict ( ON svm_iris_test AS InputTable PARTITION BY ANY ON densesvm_iris_linear_model AS Model dimension USING IdColumn ('id') TargetColumns ('[1:4]') Accumulate ('id','species') TopK (2) ) AS dt ORDER BY id, predict_confidence DESC;
Output
id predict_value predict_confidence species --- ------------- ---------------------- ---------- 15 versicolor 5.78269895802165E-001 setosa 30 setosa 7.87586385272221E-001 setosa 70 setosa 5.71041470210711E-001 versicolor 45 setosa 7.57084542015213E-001 setosa 85 versicolor 5.70149883381984E-001 versicolor 125 virginica 7.81774283286295E-001 virginica 100 versicolor 5.86286832190065E-001 versicolor 140 virginica 7.81774282914086E-001 virginica 40 setosa 7.87502148319798E-001 setosa 80 setosa 5.41079116964219E-001 versicolor 120 versicolor 5.85873625700962E-001 virginica 55 virginica 6.17964089507300E-001 versicolor 95 versicolor 4.51696914811782E-001 versicolor 135 virginica 4.62944416722431E-001 virginica 110 virginica 7.88117892019814E-001 virginica 150 virginica 4.30614034339760E-001 virginica 35 setosa 7.82679478362359E-001 setosa 50 setosa 7.81186229605767E-001 setosa 105 virginica 7.78235709537083E-001 virginica 10 setosa 7.82679062525729E-001 setosa 65 versicolor 4.20732680986458E-001 versicolor 5 setosa 7.87502768107167E-001 setosa 20 setosa 7.85358937125616E-001 setosa 60 setosa 7.33097818922085E-001 versicolor 75 virginica 5.62585972191841E-001 versicolor 115 versicolor 5.32442530321771E-001 virginica 90 setosa 6.17174718387409E-001 versicolor 130 virginica 7.87370139278602E-001 virginica 25 setosa 7.81198854595848E-001 setosa 145 virginica 7.85466732269768E-001 virginica
Download a zip file of all examples and a SQL script file that creates their input tables.